大学物理 ›› 2026, Vol. 45 ›› Issue (3): 67-.doi: 10.16854/j.cnki.1000-0712.250393

• 教学改革 • 上一篇    下一篇

知识联通在力学教学中促进深度学习的实践探索

师玉荣,王志敏,马丽珍,史晓凤,王峰   

  1. 中国海洋大学 物理与光电工程学院,山东青岛266100
  • 收稿日期:2025-07-29 修回日期:2025-09-03 出版日期:2026-05-15 发布日期:2026-06-04
  • 作者简介:师玉荣(1974—),女,山东德州人,中国海洋大学物理与光电工程学院副教授,主要从事教学研究,主讲《力学》等课程.
  • 基金资助:
    中国海洋大学本科教育教学研究重点项目:理实并重、与时创新、因材施教理念下的“力学”课程建设(2024ZD08);中国海洋大学校级本科教育教学研究重点项目:海德学院大生命类专业大学物理课程建设(2023DZ02);中国海洋大学本科教育教学研究重点项目:AI赋能大学物理教学研究与实践探索(2025ZD02);中国海洋大学本科教育教学研究重点项目:光学课程融合科研训练的教学研究(2024ZD07)资助

Practical exploration of promoting deep learning through knowledge #br# connectivity in mechanics teaching#br#

SHI Yurong, WANG Zhimin, MA Lizhen, SHI Xiaofeng, WANG Feng   

  1. College of physics and Optoelectronic Engineering, Ocean university of China, Qingdao, Shandong 266100, China
  • Received:2025-07-29 Revised:2025-09-03 Online:2026-05-15 Published:2026-06-04

摘要: 习题和例题是夯实物理学习的重要手段,现行的大学物理教学中习题总是按知识点分类,求解思路相对固化,这种做法会导致学生分析物理问题思维僵化,碰到陌生题目无从下手从而对物理产生畏难情绪,不利于学生迁移创新能力的培养.本文以力学为例提出通过例题和习题把多个知识点联通的教学设计,一个题目联通多个知识点,使学生从整体性上掌握物理概念,深入理解物理理论知识之间内在的逻辑关系,打开学生分析问题的思路,促进学生的深度学习,提高解决实际问题的能力.


关键词: 深度学习, 知识联通, 一题多解, 变质量问题

Abstract:  Exercises and example problems are crucial means for consolidating physics learning. In current university physics instruction, exercises are often categorized by knowledge points, with relatively rigid problem-solving approaches. This practice can lead to rigid thinking in students analysis of physics problems, leaving them at a loss when encountering unfamiliar questions and fostering an aversion to physics due to perceived difficulty. Consequently, it hinders the development of students ability to transfer knowledge and innovate. Using mechanics as an example, this paper proposes a teaching design that interconnects multiple knowledge points through example problems and exercises. By integrating multiple knowledge points into a single problem, students can grasp physical concepts holistically, gain a deeper understanding of the inherent logical relationships between theoretical knowledge, broaden their analytical perspectives, promote deeper learning, and enhance their ability to solve practical problems.


Key words:  deep learning, knowledge connectivity, multiple solutions for one problem, problems of variable mass